On the Evolution of Knowledge Graphs: A Survey and Perspective
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866910958046150656 |
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| author | Jiang, Xuhui Xu, Chengjin Shen, Yinghan Sun, Xun Tang, Lumingyuan Wang, Saizhuo Chen, Zhongwu Wang, Yuanzhuo Guo, Jian |
| author_facet | Jiang, Xuhui Xu, Chengjin Shen, Yinghan Sun, Xun Tang, Lumingyuan Wang, Saizhuo Chen, Zhongwu Wang, Yuanzhuo Guo, Jian |
| contents | Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_04835 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On the Evolution of Knowledge Graphs: A Survey and Perspective Jiang, Xuhui Xu, Chengjin Shen, Yinghan Sun, Xun Tang, Lumingyuan Wang, Saizhuo Chen, Zhongwu Wang, Yuanzhuo Guo, Jian Artificial Intelligence Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation. |
| title | On the Evolution of Knowledge Graphs: A Survey and Perspective |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2310.04835 |